Overview

Dataset statistics

Number of variables13
Number of observations15926
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric12

Alerts

Geography[0] is highly correlated with Geography[1]High correlation
Geography[1] is highly correlated with Geography[0]High correlation
Geography[0] is highly correlated with Geography[1] and 1 other fieldsHigh correlation
Geography[1] is highly correlated with Geography[0]High correlation
Geography[2] is highly correlated with Geography[0]High correlation
Geography[0] is highly correlated with Geography[1]High correlation
Geography[1] is highly correlated with Geography[0]High correlation
Exited is highly correlated with Geography[0] and 5 other fieldsHigh correlation
Geography[0] is highly correlated with Exited and 5 other fieldsHigh correlation
Geography[1] is highly correlated with Geography[0] and 5 other fieldsHigh correlation
Geography[2] is highly correlated with Geography[0] and 4 other fieldsHigh correlation
Gender is highly correlated with Exited and 5 other fieldsHigh correlation
Age is highly correlated with ExitedHigh correlation
Balance is highly correlated with Geography[1]High correlation
NumOfProducts is highly correlated with ExitedHigh correlation
HasCrCard is highly correlated with Exited and 5 other fieldsHigh correlation
IsActiveMember is highly correlated with Exited and 5 other fieldsHigh correlation
Exited is uniformly distributed Uniform
Geography[0] has 7293 (45.8%) zeros Zeros
Geography[1] has 10166 (63.8%) zeros Zeros
Geography[2] has 11399 (71.6%) zeros Zeros
Gender has 6386 (40.1%) zeros Zeros
Tenure has 425 (2.7%) zeros Zeros
Balance has 5097 (32.0%) zeros Zeros
HasCrCard has 3471 (21.8%) zeros Zeros
IsActiveMember has 7195 (45.2%) zeros Zeros

Reproduction

Analysis started2023-04-14 12:29:29.948192
Analysis finished2023-04-14 12:30:05.190147
Duration35.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Exited
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size124.5 KiB
1
7963 
0
7963 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15926
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
17963
50.0%
07963
50.0%

Length

2023-04-14T18:00:05.296517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-14T18:00:05.462777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
07963
50.0%
17963
50.0%

Most occurring characters

ValueCountFrequency (%)
07963
50.0%
17963
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15926
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07963
50.0%
17963
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common15926
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07963
50.0%
17963
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07963
50.0%
17963
50.0%

Geography[0]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2416
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4663632586
Minimum0
Maximum1
Zeros7293
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:05.603370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2860500817
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4726111121
Coefficient of variation (CV)1.013396968
Kurtosis-1.892849872
Mean0.4663632586
Median Absolute Deviation (MAD)0.2860500817
Skewness0.1336832634
Sum7427.301256
Variance0.2233612633
MonotonicityNot monotonic
2023-04-14T18:00:05.759577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07293
45.8%
16219
39.0%
0.89804588751
 
< 0.1%
0.46175143841
 
< 0.1%
0.69032159621
 
< 0.1%
0.021487676851
 
< 0.1%
0.87254791251
 
< 0.1%
0.30762735811
 
< 0.1%
0.41321792251
 
< 0.1%
0.52587101851
 
< 0.1%
Other values (2406)2406
 
15.1%
ValueCountFrequency (%)
07293
45.8%
0.00033433087471
 
< 0.1%
0.00074805436621
 
< 0.1%
0.0014756300661
 
< 0.1%
0.0019086500551
 
< 0.1%
0.0019127201451
 
< 0.1%
0.0020814961481
 
< 0.1%
0.0021390953461
 
< 0.1%
0.0032398273561
 
< 0.1%
0.0033365774131
 
< 0.1%
ValueCountFrequency (%)
16219
39.0%
0.99989123661
 
< 0.1%
0.99927782771
 
< 0.1%
0.99905436891
 
< 0.1%
0.99822145761
 
< 0.1%
0.99804008771
 
< 0.1%
0.99769143891
 
< 0.1%
0.99730952641
 
< 0.1%
0.99703798971
 
< 0.1%
0.99667404541
 
< 0.1%

Geography[1]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1840
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3042879937
Minimum0
Maximum1
Zeros10166
Zeros (%)63.8%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:05.900174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.9656332262
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.9656332262

Descriptive statistics

Standard deviation0.4384928776
Coefficient of variation (CV)1.44104561
Kurtosis-1.188705293
Mean0.3042879937
Median Absolute Deviation (MAD)0
Skewness0.8475897461
Sum4846.090587
Variance0.1922760037
MonotonicityNot monotonic
2023-04-14T18:00:06.040762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010166
63.8%
13922
 
24.6%
0.16039908291
 
< 0.1%
0.55230562121
 
< 0.1%
0.19215261181
 
< 0.1%
0.65880886921
 
< 0.1%
0.2799883661
 
< 0.1%
0.2298210861
 
< 0.1%
0.97489286831
 
< 0.1%
0.030818760281
 
< 0.1%
Other values (1830)1830
 
11.5%
ValueCountFrequency (%)
010166
63.8%
0.00023328990061
 
< 0.1%
0.00094563113271
 
< 0.1%
0.0017785423981
 
< 0.1%
0.0019599123051
 
< 0.1%
0.0023085611311
 
< 0.1%
0.002690473561
 
< 0.1%
0.0029620102961
 
< 0.1%
0.003095591461
 
< 0.1%
0.0033259546181
 
< 0.1%
ValueCountFrequency (%)
13922
24.6%
0.99966566911
 
< 0.1%
0.99852436991
 
< 0.1%
0.99809134991
 
< 0.1%
0.99808727991
 
< 0.1%
0.99791850391
 
< 0.1%
0.99786090471
 
< 0.1%
0.99676017261
 
< 0.1%
0.99666342261
 
< 0.1%
0.99638660271
 
< 0.1%

Geography[2]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1738
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2293487477
Minimum0
Maximum1
Zeros11399
Zeros (%)71.6%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:06.191007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3135179074
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3135179074

Descriptive statistics

Standard deviation0.3980756202
Coefficient of variation (CV)1.735678194
Kurtosis-0.2288172497
Mean0.2293487477
Median Absolute Deviation (MAD)0
Skewness1.282459674
Sum3652.608157
Variance0.1584641994
MonotonicityNot monotonic
2023-04-14T18:00:06.330301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011399
71.6%
12791
 
17.5%
0.65620825081
 
< 0.1%
0.40997388381
 
< 0.1%
0.31091372391
 
< 0.1%
0.32492078811
 
< 0.1%
0.22130758621
 
< 0.1%
0.32402886821
 
< 0.1%
0.69106858061
 
< 0.1%
0.3864807491
 
< 0.1%
Other values (1728)1728
 
10.9%
ValueCountFrequency (%)
011399
71.6%
0.00010876342141
 
< 0.1%
0.00072217234041
 
< 0.1%
0.003623401381
 
< 0.1%
0.0049334636531
 
< 0.1%
0.0049791533261
 
< 0.1%
0.005204731041
 
< 0.1%
0.0052536608561
 
< 0.1%
0.0053645411031
 
< 0.1%
0.0063197231421
 
< 0.1%
ValueCountFrequency (%)
12791
17.5%
0.99976671011
 
< 0.1%
0.99925194561
 
< 0.1%
0.99690440851
 
< 0.1%
0.99631613491
 
< 0.1%
0.99629047531
 
< 0.1%
0.9949673111
 
< 0.1%
0.99473104621
 
< 0.1%
0.99457266141
 
< 0.1%
0.99389337681
 
< 0.1%

CreditScore
Real number (ℝ≥0)

Distinct6371
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean648.0902579
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:06.466112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile495.6645322
Q1584.863021
median649
Q3711.4769639
95-th percentile801.4548831
Maximum850
Range500
Interquartile range (IQR)126.6139429

Descriptive statistics

Standard deviation91.78988081
Coefficient of variation (CV)0.1416313232
Kurtosis-0.3082183663
Mean648.0902579
Median Absolute Deviation (MAD)63.10019517
Skewness-0.07305499603
Sum10321485.45
Variance8425.382219
MonotonicityNot monotonic
2023-04-14T18:00:06.606702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850234
 
1.5%
67863
 
0.4%
65554
 
0.3%
70553
 
0.3%
66753
 
0.3%
65153
 
0.3%
68452
 
0.3%
67050
 
0.3%
68348
 
0.3%
65248
 
0.3%
Other values (6361)15218
95.6%
ValueCountFrequency (%)
3505
< 0.1%
3511
 
< 0.1%
3581
 
< 0.1%
358.19638751
 
< 0.1%
3591
 
< 0.1%
3631
 
< 0.1%
3651
 
< 0.1%
366.17622711
 
< 0.1%
3671
 
< 0.1%
370.75798641
 
< 0.1%
ValueCountFrequency (%)
850234
1.5%
849.9947311
 
< 0.1%
849.47740511
 
< 0.1%
849.46899621
 
< 0.1%
849.15164991
 
< 0.1%
8498
 
0.1%
848.58773081
 
< 0.1%
848.14797051
 
< 0.1%
8485
 
< 0.1%
847.74985871
 
< 0.1%

Gender
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2929
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5057863927
Minimum0
Maximum1
Zeros6386
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:06.755428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5334116911
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4680177967
Coefficient of variation (CV)0.9253269829
Kurtosis-1.890298452
Mean0.5057863927
Median Absolute Deviation (MAD)0.4665883089
Skewness-0.02186211022
Sum8055.15409
Variance0.2190406581
MonotonicityNot monotonic
2023-04-14T18:00:06.911639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16613
41.5%
06386
40.1%
0.85899044651
 
< 0.1%
0.82093494121
 
< 0.1%
0.73267505421
 
< 0.1%
0.90921275311
 
< 0.1%
0.83206575061
 
< 0.1%
0.14458499731
 
< 0.1%
0.72001120631
 
< 0.1%
0.83953550171
 
< 0.1%
Other values (2919)2919
18.3%
ValueCountFrequency (%)
06386
40.1%
0.00072217234041
 
< 0.1%
0.00074805436621
 
< 0.1%
0.0011345263311
 
< 0.1%
0.0014756300661
 
< 0.1%
0.0019512193891
 
< 0.1%
0.0019599123051
 
< 0.1%
0.0020485049391
 
< 0.1%
0.0023085611311
 
< 0.1%
0.003623401381
 
< 0.1%
ValueCountFrequency (%)
16613
41.5%
0.99966566911
 
< 0.1%
0.99889729321
 
< 0.1%
0.9988910011
 
< 0.1%
0.99841325211
 
< 0.1%
0.99821083811
 
< 0.1%
0.99813136511
 
< 0.1%
0.99809134991
 
< 0.1%
0.99805599511
 
< 0.1%
0.99791940461
 
< 0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5826
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.16053007
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:07.067889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q134
median40
Q347.38349676
95-th percentile59
Maximum92
Range74
Interquartile range (IQR)13.38349676

Descriptive statistics

Standard deviation10.10249507
Coefficient of variation (CV)0.2454413257
Kurtosis0.4603700809
Mean41.16053007
Median Absolute Deviation (MAD)6.90210794
Skewness0.5510800769
Sum655522.6018
Variance102.0604066
MonotonicityNot monotonic
2023-04-14T18:00:07.206610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37488
 
3.1%
38481
 
3.0%
35479
 
3.0%
36459
 
2.9%
40454
 
2.9%
34449
 
2.8%
33449
 
2.8%
39425
 
2.7%
32422
 
2.6%
31404
 
2.5%
Other values (5816)11416
71.7%
ValueCountFrequency (%)
1822
 
0.1%
1927
 
0.2%
2040
0.3%
20.401246621
 
< 0.1%
2153
0.3%
21.099442321
 
< 0.1%
2284
0.5%
22.003165761
 
< 0.1%
22.901320761
 
< 0.1%
2399
0.6%
ValueCountFrequency (%)
922
< 0.1%
881
 
< 0.1%
851
 
< 0.1%
842
< 0.1%
831
 
< 0.1%
821
 
< 0.1%
814
< 0.1%
803
< 0.1%
79.775494141
 
< 0.1%
794
< 0.1%

Tenure
Real number (ℝ≥0)

ZEROS

Distinct5402
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.009653933
Minimum0
Maximum10
Zeros425
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:07.355675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.714424876
Coefficient of variation (CV)0.5418388001
Kurtosis-1.056335205
Mean5.009653933
Median Absolute Deviation (MAD)2
Skewness0.01933717954
Sum79783.74854
Variance7.368102407
MonotonicityNot monotonic
2023-04-14T18:00:07.503440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11106
 
6.9%
21101
 
6.9%
81078
 
6.8%
51072
 
6.7%
41065
 
6.7%
31061
 
6.7%
71057
 
6.6%
91053
 
6.6%
61013
 
6.4%
10504
 
3.2%
Other values (5392)5816
36.5%
ValueCountFrequency (%)
0425
2.7%
0.010291278541
 
< 0.1%
0.016006881581
 
< 0.1%
0.019440049141
 
< 0.1%
0.020019464481
 
< 0.1%
0.021634962981
 
< 0.1%
0.039217860451
 
< 0.1%
0.054639562261
 
< 0.1%
0.059601688871
 
< 0.1%
0.096295374661
 
< 0.1%
ValueCountFrequency (%)
10504
3.2%
9.997048741
 
< 0.1%
9.9941463421
 
< 0.1%
9.9863907461
 
< 0.1%
9.9813664381
 
< 0.1%
9.9794607981
 
< 0.1%
9.9720038731
 
< 0.1%
9.9709465341
 
< 0.1%
9.9615880581
 
< 0.1%
9.9597384881
 
< 0.1%

Balance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10828
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81696.39741
Minimum0
Maximum250898.09
Zeros5097
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:07.644029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median102991.935
Q3128829.7087
95-th percentile164851.9231
Maximum250898.09
Range250898.09
Interquartile range (IQR)128829.7087

Descriptive statistics

Standard deviation61307.63871
Coefficient of variation (CV)0.7504325852
Kurtosis-1.355026414
Mean81696.39741
Median Absolute Deviation (MAD)37905.78283
Skewness-0.2778562518
Sum1301096825
Variance3758626564
MonotonicityNot monotonic
2023-04-14T18:00:07.786637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05097
32.0%
105473.742
 
< 0.1%
130170.822
 
< 0.1%
153856.40691
 
< 0.1%
134796.871
 
< 0.1%
123509.67171
 
< 0.1%
113905.481
 
< 0.1%
110132.551
 
< 0.1%
161027.57531
 
< 0.1%
109899.2511
 
< 0.1%
Other values (10818)10818
67.9%
ValueCountFrequency (%)
05097
32.0%
639.4772361
 
< 0.1%
3768.691
 
< 0.1%
9818.9524771
 
< 0.1%
12100.210621
 
< 0.1%
12449.869851
 
< 0.1%
12459.191
 
< 0.1%
14262.81
 
< 0.1%
15614.98331
 
< 0.1%
16214.698841
 
< 0.1%
ValueCountFrequency (%)
250898.091
< 0.1%
240966.88741
< 0.1%
238387.561
< 0.1%
236158.89251
< 0.1%
235473.81431
< 0.1%
224387.73821
< 0.1%
222267.631
< 0.1%
221532.81
< 0.1%
221317.97041
< 0.1%
220803.96431
< 0.1%

NumOfProducts
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2825
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.509791874
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:07.927228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.032883895
Q32
95-th percentile2.681874665
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6103640999
Coefficient of variation (CV)0.4042703571
Kurtosis0.9593841808
Mean1.509791874
Median Absolute Deviation (MAD)0.03288389526
Skewness1.052813248
Sum24044.94538
Variance0.3725443345
MonotonicityNot monotonic
2023-04-14T18:00:08.067824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17902
49.6%
24787
30.1%
3348
 
2.2%
468
 
0.4%
2.0630388141
 
< 0.1%
1.1575572661
 
< 0.1%
1.2423777831
 
< 0.1%
2.8558712951
 
< 0.1%
1.2342218961
 
< 0.1%
1.4795354431
 
< 0.1%
Other values (2815)2815
 
17.7%
ValueCountFrequency (%)
17902
49.6%
1.0009456311
 
< 0.1%
1.000952981
 
< 0.1%
1.0011644041
 
< 0.1%
1.0021390951
 
< 0.1%
1.0022054141
 
< 0.1%
1.0032398271
 
< 0.1%
1.0033365771
 
< 0.1%
1.0036234011
 
< 0.1%
1.0036838651
 
< 0.1%
ValueCountFrequency (%)
468
0.4%
3.9906457771
 
< 0.1%
3.9794607981
 
< 0.1%
3.9765853091
 
< 0.1%
3.9725266931
 
< 0.1%
3.9720496581
 
< 0.1%
3.9687233981
 
< 0.1%
3.9587338541
 
< 0.1%
3.9548089581
 
< 0.1%
3.9515442941
 
< 0.1%

HasCrCard
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2552
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7019447133
Minimum0
Maximum1
Zeros3471
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:08.255083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2008734728
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.7991265272

Descriptive statistics

Standard deviation0.426975975
Coefficient of variation (CV)0.6082757899
Kurtosis-1.096652254
Mean0.7019447133
Median Absolute Deviation (MAD)0
Skewness-0.8764191325
Sum11179.1715
Variance0.1823084832
MonotonicityNot monotonic
2023-04-14T18:00:08.395677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19905
62.2%
03471
 
21.8%
0.97489286831
 
< 0.1%
0.91316839151
 
< 0.1%
0.061339487781
 
< 0.1%
0.18241142541
 
< 0.1%
0.037215579661
 
< 0.1%
0.83967864721
 
< 0.1%
0.34740442481
 
< 0.1%
0.36011337591
 
< 0.1%
Other values (2542)2542
 
16.0%
ValueCountFrequency (%)
03471
21.8%
0.00023328990061
 
< 0.1%
0.00058220222211
 
< 0.1%
0.00074805436621
 
< 0.1%
0.0014756300661
 
< 0.1%
0.0017785423981
 
< 0.1%
0.0018686349481
 
< 0.1%
0.0019512193891
 
< 0.1%
0.0025515755331
 
< 0.1%
0.0036058271641
 
< 0.1%
ValueCountFrequency (%)
19905
62.2%
0.99962064211
 
< 0.1%
0.99805599511
 
< 0.1%
0.99786090471
 
< 0.1%
0.99676017261
 
< 0.1%
0.99647876491
 
< 0.1%
0.99629047531
 
< 0.1%
0.99600626311
 
< 0.1%
0.99567799291
 
< 0.1%
0.99502084671
 
< 0.1%

IsActiveMember
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2812
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4595847911
Minimum0
Maximum1
Zeros7195
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:08.536267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2787612672
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.467535181
Coefficient of variation (CV)1.017299071
Kurtosis-1.86798537
Mean0.4595847911
Median Absolute Deviation (MAD)0.2787612672
Skewness0.1604349596
Sum7319.347384
Variance0.2185891455
MonotonicityNot monotonic
2023-04-14T18:00:08.692481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07195
45.2%
15921
37.2%
0.47117754281
 
< 0.1%
0.28929479461
 
< 0.1%
0.55011666631
 
< 0.1%
0.85780628531
 
< 0.1%
0.83548037971
 
< 0.1%
0.58548670481
 
< 0.1%
0.41463942031
 
< 0.1%
0.64607998691
 
< 0.1%
Other values (2802)2802
 
17.6%
ValueCountFrequency (%)
07195
45.2%
0.00037935788121
 
< 0.1%
0.00078032529611
 
< 0.1%
0.00094563113271
 
< 0.1%
0.0019086500551
 
< 0.1%
0.0019127201451
 
< 0.1%
0.0019440049141
 
< 0.1%
0.0020485049391
 
< 0.1%
0.0021390953461
 
< 0.1%
0.002690473561
 
< 0.1%
ValueCountFrequency (%)
15921
37.2%
0.99976671011
 
< 0.1%
0.99966566911
 
< 0.1%
0.99952350991
 
< 0.1%
0.99941779781
 
< 0.1%
0.99889729321
 
< 0.1%
0.99841325211
 
< 0.1%
0.99822145761
 
< 0.1%
0.99804878061
 
< 0.1%
0.99804008771
 
< 0.1%

EstimatedSalary
Real number (ℝ≥0)

Distinct15925
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100449.7306
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size124.5 KiB
2023-04-14T18:00:08.833075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9953.618414
Q151218.40217
median100598.4511
Q3149941.7706
95-th percentile190284.0885
Maximum199992.48
Range199980.9
Interquartile range (IQR)98723.36846

Descriptive statistics

Standard deviation57515.84687
Coefficient of variation (CV)0.5725833858
Kurtosis-1.18931804
Mean100449.7306
Median Absolute Deviation (MAD)49370.70606
Skewness-0.002124005136
Sum1599762410
Variance3308072641
MonotonicityNot monotonic
2023-04-14T18:00:08.989286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.922
 
< 0.1%
15217.570951
 
< 0.1%
160162.421
 
< 0.1%
172988.86571
 
< 0.1%
65069.031
 
< 0.1%
82774.071
 
< 0.1%
165693.061
 
< 0.1%
24495.031
 
< 0.1%
60407.931
 
< 0.1%
76700.936431
 
< 0.1%
Other values (15915)15915
99.9%
ValueCountFrequency (%)
11.581
< 0.1%
87.780401951
< 0.1%
90.071
< 0.1%
91.751
< 0.1%
96.271
< 0.1%
106.671
< 0.1%
123.071
< 0.1%
142.811
< 0.1%
143.341
< 0.1%
178.191
< 0.1%
ValueCountFrequency (%)
199992.481
< 0.1%
199970.741
< 0.1%
199953.331
< 0.1%
199929.171
< 0.1%
199909.321
< 0.1%
199862.751
< 0.1%
199857.471
< 0.1%
199841.321
< 0.1%
199808.11
< 0.1%
199805.631
< 0.1%

Interactions

2023-04-14T18:00:02.664389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:31.297784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:43.423482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:45.378670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:47.227450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:49.053401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:51.116040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:52.961128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:54.762696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:56.646038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:59.023963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:00.937110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:02.851844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:31.547724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:43.595288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:45.534921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:47.362977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:49.236019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:51.265238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:53.087963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:54.903289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:56.849159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:59.178255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:01.077705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:03.023679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:31.709995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:43.751501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:45.675478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:47.503588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:49.423476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:51.416135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:53.244134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:55.056749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:57.323444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:59.341127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:01.210406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:03.172911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:31.866208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:43.907276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:45.817586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:47.645369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:49.610934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:51.568317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:53.415974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:55.215734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:57.682702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:59.481759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:01.351004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:03.313536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:32.006846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:44.048027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:45.940335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:47.785962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:49.752949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:51.708908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:53.572219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:55.369557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:57.823298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:59.606724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:01.509655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:03.469759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:32.180021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:44.188627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:46.082235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:47.926288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:49.877918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:51.849532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:53.712778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:55.494522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:57.948263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:59.731661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:01.651260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:03.625962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:32.336235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:44.329250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:46.240740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:48.113747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:50.068522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:52.025027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:53.853401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:55.650736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:58.106463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:59.982830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:01.807470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:03.758599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:32.476826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:44.469845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:46.396914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:48.287261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:50.238014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:52.193766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:53.993762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:55.780483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:58.248764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:00.163031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:01.963726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:03.914849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:32.617420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:44.594810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:46.578585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:48.412196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:50.403772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:52.351440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:54.134356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:55.936660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:58.392050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:00.338217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:02.110289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:04.086651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:32.773508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:44.800265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:46.769724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:48.554527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:50.622471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:52.492030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:54.290603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:56.120265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:58.533981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:00.478772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:02.234712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:04.251843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:32.945348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:44.962883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:46.909890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:48.757575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:50.794308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:52.648245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:54.434691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:56.279427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:58.692622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:00.619364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:02.375297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:04.408087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:33.085967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:45.120545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:47.055622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:48.901418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:50.944204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:52.787818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:54.575239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:56.442959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T17:59:58.864468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:00.765243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-14T18:00:02.500275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2023-04-14T18:00:09.140481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-04-14T18:00:09.427832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-04-14T18:00:09.693395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-04-14T18:00:09.958989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-04-14T18:00:04.682905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-14T18:00:05.010990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ExitedGeography[0]Geography[1]Geography[2]CreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalary
011.00.00.0619.00.042.02.00.001.01.01.0101348.88
100.00.01.0608.00.041.01.083807.861.00.01.0112542.58
211.00.00.0502.00.042.08.0159660.803.01.00.0113931.57
301.00.00.0699.00.039.01.00.002.00.00.093826.63
400.00.01.0850.00.043.02.0125510.821.01.01.079084.10
510.00.01.0645.01.044.08.0113755.782.01.00.0149756.71
601.00.00.0822.01.050.07.00.002.01.01.010062.80
710.01.00.0376.00.029.04.0115046.744.01.00.0119346.88
801.00.00.0501.01.044.04.0142051.072.00.01.074940.50
901.00.00.0684.01.027.02.0134603.881.01.01.071725.73

Last rows

ExitedGeography[0]Geography[1]Geography[2]CreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalary
1591610.0000001.0000000.0567.6506430.00000033.9606962.000000115557.8406161.2882090.0960700.903930112615.951847
1591710.9982210.0017790.0659.9644290.00000047.9911070.01600790052.6936801.9982210.0017790.998221187598.569720
1591810.0419600.9580400.0779.3985030.04196052.6223631.08391981761.1782891.0000001.0000000.000000182841.899725
1591910.0000001.0000000.0746.4299500.78169761.2326125.345092112023.5407431.0000001.0000000.21830335964.861659
1592011.0000000.0000000.0571.5117990.00000041.5001939.2502900.0000001.7500970.2499030.000000170023.960197
1592110.7373460.2626540.0662.6963111.00000049.2120395.313268150334.3447302.4746931.0000000.737346124075.996213
1592210.0000000.0000001.0594.0076510.00000040.3190512.3343530.0000001.0000000.6671771.000000130144.261430
1592310.2271770.7728230.0677.7324190.77282344.1358871.772823167502.0691931.0000000.2271770.000000117569.480952
1592410.9476150.0523850.0723.3424390.05238561.1618434.104770140577.1099991.0000000.9476150.00000031178.255036
1592510.0000001.0000000.0630.4035110.00000052.8070167.070176114077.0102381.0000001.0000000.535088107400.827473